We propose an interactive methodology for generating counterfactual explanations for univariate time series data in classification tasks by leveraging 2D projections and decision boundary maps to tackle interpretability challenges. Our approach aims to enhance the transparency and understanding of deep learning models' decision processes. The application simplifies the time series data analysis by enabling users to interactively manipulate projected data points, providing intuitive insights through inverse projection techniques. By abstracting user interactions with the projected data points rather than the raw time series data, our method facilitates an intuitive generation of counterfactual explanations. This approach allows for a more straightforward exploration of univariate time series data, enabling users to manipulate data points to comprehend potential outcomes of hypothetical scenarios. We validate this method using the ECG5000 benchmark dataset, demonstrating significant improvements in interpretability and user understanding of time series classification. The results indicate a promising direction for enhancing explainable AI, with potential applications in various domains requiring transparent and interpretable deep learning models. Future work will explore the scalability of this method to multivariate time series data and its integration with other interpretability techniques.

翻译：本文提出了一种交互式方法，通过利用二维投影和决策边界映射来解决可解释性挑战，为分类任务中的单变量时间序列数据生成反事实解释。我们的方法旨在增强深度学习模型决策过程的透明度和可理解性。该应用通过允许用户交互式地操作投影数据点，并借助逆投影技术提供直观洞察，从而简化了时间序列数据分析。通过抽象用户与投影数据点（而非原始时间序列数据）的交互，我们的方法促进了反事实解释的直观生成。该方法支持对单变量时间序列数据进行更直接的探索，使用户能够通过操作数据点来理解假设情景的潜在结果。我们使用ECG5000基准数据集验证了该方法，结果表明其在时间序列分类的可解释性和用户理解方面取得了显著改进。这些结果为增强可解释人工智能指明了一个有前景的方向，在需要透明且可解释的深度学习模型的多个领域具有潜在应用价值。未来工作将探索该方法向多变量时间序列数据的可扩展性，以及其与其他可解释性技术的集成。